A magnetic levitation motor control method, device and magnetic levitation motor

Abstract

The present application provides a magnetic levitation motor control method, device and magnetic levitation motor, relating to the technical field of magnetic levitation motor control. The key points of its technical solution are as follows: detecting the change rate of the grid voltage and determining the voltage fluctuation mode based on a preset threshold; reading the environmental RFID tag information through a scene coding analysis unit to obtain the current scene coding; retrieving the maximum compensation current value, current change rate threshold and PID integral time constant from a preset parameter matrix based on the voltage fluctuation mode and the scene coding; limiting the change rate of the motor winding current according to the current change rate threshold, and performing motor control through the maximum compensation current value and the PID integral time constant. The magnetic levitation motor control method, device and magnetic levitation motor provided by the present application have the advantages of improving the operation stability of the motor under voltage fluctuations and multi-noise scenarios, suppressing electromagnetic noise and adapting to different scenarios.

Description

Technical Field

[0001] This application relates to the technical field of magnetic levitation motor control. Specifically, it relates to a magnetic levitation motor control method, device, and magnetic levitation motor. Background Art

[0002] During the operation of a low-power distilled water machine, the power grid load fluctuation and the noise control requirements in multiple scenarios pose dual challenges to the stability and quietness of the magnetic levitation motor. Traditional control methods use a PID strategy with fixed parameters, which can maintain basic operation when the power grid voltage is stable, but have significant defects when the voltage fluctuates violently. Since the low-power motor winding adopts a low-resistance design to improve energy efficiency, its inductance value decreases, resulting in a significant increase in the current change rate, and a tiny voltage fluctuation can trigger a current mutation. This mutation not only exacerbates electromagnetic noise but may also cause the motor to become unstable due to current overshoot. Especially in medical or laboratory scenarios where the noise sensitivity requirement is extremely high, the traditional method cannot meet the requirements of both stable operation of the equipment and dynamic noise suppression.

[0003] In the prior art, to cope with voltage dips, a compensation method of increasing the drive current is often adopted, but this method causes the temperature rise of the motor winding to accelerate, which in turn exacerbates the stator resistance drift, forming a vicious cycle of parameter mismatch. On the other hand, simply restricting the current change rate can reduce noise, but it cannot adapt to the noise threshold differences in different scenarios. For example, the medium noise level acceptable in a home scenario may exceed the allowable range in a medical scenario, and the fixed-parameter control lacks the ability to identify and adaptively adjust to the operating scenario. In addition, the traditional method does not consider the influence of the load state (such as water level, water temperature) on the motor parameter drift, resulting in a mismatch between the compensation parameters and the real-time working conditions, further weakening the control accuracy. These limitations make it difficult for low-power magnetic levitation motors to balance energy efficiency, stability, and quietness in complex power grid environments and multi-scenario applications.

[0004] In view of the above problems, the prior art urgently needs to be improved. Summary of the Invention

[0005] The purpose of this application is to provide a magnetic levitation motor control method, device, and magnetic levitation motor, which have the advantages of improving the operating stability of the motor under voltage fluctuations and multi-noise scenarios, suppressing electromagnetic noise, and adapting to the requirements of different scenarios.

[0006] This application provides a magnetic levitation motor control method for controlling the operation of the magnetic levitation motor of a low-power distilled water machine in scenarios where the power grid voltage fluctuation exceeds a threshold and multiple noise thresholds. The technical solution is as follows:

[0007] Detect the change rate of the power grid voltage and determine the voltage fluctuation mode based on a preset threshold;

[0008] Read the environmental RFID tag information through the scene coding parsing unit to obtain the current scene coding;

[0009] Based on the voltage fluctuation pattern and the scene coding, retrieve the maximum compensation current value, the current change rate threshold, and the PID integral time constant from the preset parameter matrix;

[0010] Limit the current change rate of the motor winding according to the current change rate threshold, and perform motor control through the maximum compensation current value and the PID integral time constant.

[0011] Further, the present application also proposes that the step of retrieving the maximum compensation current value, the current change rate threshold, and the PID integral time constant from the preset parameter matrix based on the voltage fluctuation pattern and the scene coding includes:

[0012] Collect motor operation data in real time, where the motor operation data includes the motor winding temperature, the motor speed, and the stator current;

[0013] Based on the motor winding temperature, the motor speed, and the stator current, calculate the motor parameter drift amount, where the motor parameters include the stator resistance, the mutual inductance, and the back electromotive force coefficient;

[0014] According to the motor parameter drift amount, update the preset parameter matrix by using the least squares method, where the preset parameter matrix includes the maximum compensation current value, the current change rate threshold, and the PID integral time constant under different combinations of voltage fluctuation patterns and scene codings;

[0015] In response to the change of the voltage fluctuation pattern and the scene coding, query the updated preset parameter matrix to obtain the corresponding maximum compensation current value, the current change rate threshold, and the PID integral time constant.

[0016] Further, the present application also proposes that the step of updating the preset parameter matrix by using the least squares method according to the motor parameter drift amount, where the preset parameter matrix includes the maximum compensation current value, the current change rate threshold, and the PID integral time constant under different combinations of voltage fluctuation patterns and scene codings includes:

[0017] Obtain the current load state of the distilled water machine, where the load state includes the water level and the water temperature;

[0018] Based on the water level and the water temperature, determine the motor parameter drift weight coefficients corresponding to the current load state, where the motor parameter drift weight coefficients include the stator resistance weight coefficient, the mutual inductance weight coefficient, and the back electromotive force coefficient weight coefficient;

[0019] Update the preset parameter matrix by using the weighted least squares method according to the motor parameter drift amount and the motor parameter drift weight coefficient. The preset parameter matrix includes the maximum compensation current value, the current change rate threshold, and the PID integral time constant under different voltage fluctuation modes and scenario coding combinations.

[0020] Further, the present application also proposes that the step of detecting the grid voltage change rate and determining the voltage fluctuation mode based on a preset threshold includes:

[0021] Collect grid voltage data in real time, and filter the grid voltage data by using a moving average filtering algorithm;

[0022] Calculate the change rate of the filtered voltage data within a preset time window to obtain the voltage change rate;

[0023] Perform spectrum analysis on the voltage change rate to extract the main frequency component of the voltage fluctuation;

[0024] When the voltage change rate exceeds the preset threshold and the main frequency component is lower than the preset frequency threshold, determine the voltage fluctuation mode.

[0025] Further, the present application also proposes that the step of updating the preset parameter matrix by using the weighted least squares method according to the motor parameter drift amount and the motor parameter drift weight coefficient, where the preset parameter matrix includes the maximum compensation current value, the current change rate threshold, and the PID integral time constant under different voltage fluctuation modes and scenario coding combinations includes:

[0026] Construct an objective function, where the objective function takes the maximum compensation current value, the current change rate threshold, and the PID integral time constant in the preset parameter matrix as independent variables, and the motor parameter drift amount and the motor parameter drift weight coefficient as constraint conditions, and the objective function is used to minimize the motor control error;

[0027] Use the sequential quadratic programming algorithm to solve the objective function to obtain the updated preset parameter matrix, where the preset parameter matrix includes the maximum compensation current value, the current change rate threshold, and the PID integral time constant under different voltage fluctuation modes and scenario coding combinations.

[0028] Further, the present application also proposes that the step of retrieving the maximum compensation current value, the current change rate threshold, and the PID integral time constant from the preset parameter matrix based on the voltage fluctuation mode and the scenario coding includes:

[0029] Determine the voltage level corresponding to the voltage fluctuation mode and the noise level corresponding to the scenario coding;

[0030] Match in the index table of the preset parameter matrix based on the voltage level and the noise level, where the index table stores the mapping relationship between the voltage level, the noise level and the parameter storage address;

[0031] In response to the existence of a parameter storage address in the index table that matches both the voltage level and the noise level, read the maximum compensation current value, the current change rate threshold, and the PID integral time constant from the parameter storage address.

[0032] Further, the present application also proposes that the step of restricting the current change rate of the motor winding according to the current change rate threshold and performing motor control through the maximum compensation current value and the PID integral time constant includes:

[0033] Calculate the difference between the actual change rate of the motor winding current and the current change rate threshold;

[0034] When the difference is greater than zero, start the current gradient limiter, reduce the motor winding voltage by adjusting the PWM duty cycle, suppress the actual change rate of the motor winding current, and make the actual change rate converge to the current change rate threshold;

[0035] Based on the maximum compensation current value, calculate the target current value of the PID controller, and adjust the output voltage of the PID controller according to the target current value and the PID integral time constant to compensate for the influence of voltage fluctuation on the operation of the motor.

[0036] Further, the present application also proposes that the step of, when the difference is greater than zero, starting the current gradient limiter, reducing the motor winding voltage by adjusting the PWM duty cycle, suppressing the actual change rate of the motor winding current, and making the actual change rate converge to the current change rate threshold includes:

[0037] When the difference is greater than zero, start the current gradient limiter, and determine the PWM duty cycle adjustment rate in response to the voltage fluctuation mode. The voltage sudden drop mode corresponds to a fast adjustment rate, and the voltage slow change mode corresponds to a gentle adjustment rate;

[0038] Based on the scenario coding, determine the PWM switching frequency;

[0039] Adjust the PWM duty cycle according to the PWM duty cycle adjustment rate, and drive the motor winding voltage at the PWM switching frequency to suppress the actual change rate of the motor winding current, and make the actual change rate converge to the current change rate threshold.

[0040] Further, the present application also proposes a magnetic levitation motor control device for controlling the operation of the magnetic levitation motor of a low-power distilled water machine in a scenario where the grid voltage fluctuation exceeds the threshold and the multi-noise threshold, including:

[0041] A voltage detection unit for detecting the grid voltage change rate and determining the voltage fluctuation mode based on a preset threshold;

[0042] A scene coding analysis unit, configured to read environmental RFID tag information and obtain the current scene coding;

[0043] A parameter retrieval unit, configured to retrieve a maximum compensation current value, a current change rate threshold, and a PID integral time constant from a preset parameter matrix based on a voltage fluctuation pattern and the scene coding;

[0044] A control execution unit, configured to limit the current change rate of the motor winding current according to the current change rate threshold and execute motor control through the maximum compensation current value and the PID integral time constant.

[0045] Furthermore, the present application also proposes a magnetic levitation motor to which the above method is applied.

[0046] As can be seen from the above, a magnetic levitation motor control method, device, and magnetic levitation motor provided by the present application dynamically adjust control parameters by detecting the power grid voltage fluctuation pattern and combining the scene coding, limit the current change rate, and adaptively compensate for voltage fluctuations, effectively suppressing electromagnetic noise and improving the motor stability, and having the beneficial effects of adapting to multi-scene requirements, improving operation accuracy, and reducing the risk of parameter mismatch. Description of the Drawings

[0047] Figure 1 It is a schematic flowchart of a magnetic levitation motor control method provided by the present application.

[0048] Figure 2 It is a schematic structural diagram of a magnetic levitation motor control device provided by the present application.

[0049] In the figure: 210, a voltage detection unit; 220, a scene coding analysis unit; 230, a parameter retrieval unit; 240, a control execution unit. Detailed Embodiments

[0050] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application usually described and illustrated in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0051] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0052] In the control of the magnetic levitation motor of a traditional distilled water machine, the PID control strategy with fixed parameters cannot adapt to the dynamic coupling of grid voltage fluctuations and multi-scenario noise constraints. When the amplitude of the grid voltage fluctuation exceeds the threshold, the rate of voltage change causes a sudden change in the motor winding current, and the low-resistance winding design further amplifies the rate of current change. At the same time, there are significant differences in the threshold limits of electromagnetic noise in scenarios such as medical areas and laboratories. Fixed control parameters are difficult to simultaneously meet the dual requirements of rapid current compensation and noise suppression. The separate processing of voltage fluctuation detection and scenario noise constraints results in the motor being forced to select a sub-optimal control state of current overshoot or noise overshoot when the voltage drops suddenly.

[0053] For example, when operating during the low-load period of the local grid at night in a medical area, the simultaneous shutdown of multiple high-power devices causes a sudden increase in the local grid voltage. At this time, the magnetic levitation motor of the distilled water machine needs to quickly increase the drive current to maintain suspension stability. However, the threshold limit requirements of the medical environment for electromagnetic noise require that the rate of current change be lower than that in conventional scenarios. The traditional control method does not associate the scenario code when retrieving the parameter matrix and only selects the compensation current based on the voltage fluctuation pattern, resulting in the current gradient exceeding the noise threshold in the medical area and triggering an equipment alarm. If the rate of current change is forcibly restricted, the motor will have an axial displacement deviation due to insufficient compensation, and finally trigger a protection shutdown.

[0054] In response to this, referring to Figure 1 , the present application proposes a magnetic levitation motor control method for controlling the operation of the magnetic levitation motor of a low-power distilled water machine in scenarios where the grid voltage fluctuation exceeds the threshold and multiple noise thresholds, including the following steps:

[0055] S110. Detect the rate of change of the grid voltage and determine the voltage fluctuation pattern based on a preset threshold;

[0056] S120. Read the environmental RFID tag information through the scenario coding analysis unit to obtain the current scenario code;

[0057] S130. Based on the voltage fluctuation pattern and the scenario code, retrieve the maximum compensation current value, the current change rate threshold, and the PID integral time constant from a preset parameter matrix;

[0058] S140. Limit the rate of change of the motor winding current according to the current change rate threshold and perform motor control through the maximum compensation current value and the PID integral time constant.

[0059] Among them, the detection of the power grid voltage change rate refers to identifying the abnormal voltage state by real-time monitoring the fluctuation speed of the power grid voltage. Specifically, it can be achieved by using a differential voltage sensor in combination with a sliding window mean algorithm, which is used to judge the voltage sag or slow change mode so as to trigger different control strategies.

[0060] Among them, the scene coding parsing unit refers to obtaining the noise level identifier of the environment where the device is located through radio frequency identification technology. Specifically, it can be achieved by using a UHF-band RFID reader and an encrypted tag communication protocol, which is used to dynamically identify the noise threshold limits of different scenarios such as medical areas and laboratories.

[0061] Among them, the preset parameter matrix refers to a database structure for storing multi-dimensional control parameters. Specifically, it can be achieved by using a three-dimensional array to establish an index table according to the voltage mode, scene coding, and load status, which is used to quickly match the optimal control parameter combination under the current working conditions.

[0062] Among them, the current change rate threshold limit refers to the dynamic constraint on the rising rate of the motor drive current. Specifically, it can be achieved by using a gradient observer in cooperation with a PWM duty cycle closed-loop regulation, which is used to suppress the problem of excessive electromagnetic noise caused by current mutation.

[0063] Among them, the PID integral time constant adjustment refers to dynamically correcting the integral response speed of the controller according to the voltage fluctuation characteristics. Specifically, it can be achieved by combining fuzzy logic with an online parameter tuning algorithm, which is used to balance the contradiction between the voltage compensation speed and the system stability.

[0064] The core innovation of this application lies in establishing a dual matching mechanism for voltage fluctuation patterns and scene coding, and realizing the dynamic coupling adjustment of control parameters through a preset parameter matrix, while suppressing the noise exceeding the standard caused by current mutation and maintaining the stable operation of the motor under power grid fluctuations.

[0065] The working process and principle of this application are as follows: First, detect the power grid voltage change rate, and judge the voltage fluctuation pattern through a preset threshold. The voltage detection unit continuously samples the power grid voltage and calculates the voltage change rate between adjacent sampling points. When the change rate exceeds the preset threshold, the voltage fluctuation pattern determination is triggered. The voltage fluctuation patterns include types such as voltage sag, voltage swell, and slow voltage change.

[0066] Secondly, the scene coding parsing unit reads the environmental RFID tag information to obtain the current scene coding. The RFID reader scans the pre-set RFID tags in the environment and decodes to obtain the scene coding. The scene coding corresponds to different application environments, such as medical areas, laboratories, office areas, etc., and each scene has specific noise threshold requirements.

[0067] Further, based on the voltage fluctuation pattern and scenario encoding, retrieve the maximum compensation current value, current change rate threshold, and PID integral time constant from the preset parameter matrix. The parameter matrix pre-stores the optimal control parameters under different combinations of voltage fluctuation patterns and scenario encodings. Through the combined index of the voltage fluctuation pattern and scenario encoding, quickly retrieve the control parameters suitable for the current working condition.

[0068] Finally, limit the change rate of the motor winding current according to the current change rate threshold, and perform motor control through the maximum compensation current value and PID integral time constant. The current gradient limiter restricts the current change speed according to the current change rate threshold to suppress electromagnetic noise. The PID controller sets the target current based on the maximum compensation current value and optimizes the dynamic response characteristics by adjusting the integral time constant.

[0069] Thus, the present application realizes the dynamic adaptation of control parameters to voltage fluctuations and scenario requirements, overcoming the limitations of traditional fixed-parameter control. Through the dual judgment mechanism of voltage fluctuation detection and scenario encoding recognition, the coordinated optimization of motor control and noise suppression is achieved.

[0070] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0071] The voltage detection unit uses a high-precision voltage sampling chip. The voltage change rate threshold is set to 5V / ms to trigger the determination of the voltage fluctuation pattern. The voltage dip mode corresponds to a change rate less than -5V / ms, the voltage surge mode corresponds to a change rate greater than 5V / ms, and the slow voltage change mode corresponds to a change rate between ±5V / ms.

[0072] The scenario encoding parsing unit uses an RFID card reading module. RFID tags are pre-set in the environment. For example, the medical area is encoded as 0x01, the laboratory is encoded as 0x02, the office area is encoded as 0x03, etc. The RFID reader scans the environmental tags every 5 seconds to update the scenario encoding.

[0073] The parameter matrix is stored using a two-dimensional array, with the row index being the voltage fluctuation pattern and the column index being the scenario encoding. The matrix elements include the maximum compensation current value, current change rate threshold, and PID integral time constant. For example, the parameter combination for the medical area in the voltage dip mode is {2A, 0.5A / ms, 10ms}, and the parameter combination for the laboratory is {2.5A, 1A / ms, 8ms}.

[0074] The current gradient limiter realizes the control of the current change rate by adjusting the PWM duty cycle. When the actual current change rate exceeds the threshold, the limiter linearly reduces the PWM duty cycle until the current change rate converges to the threshold.

[0075] The PID controller adopts the incremental PID algorithm. The integral time constant can be dynamically adjusted within the range of 5 ms - 20 ms to optimize the dynamic response characteristics of the motor. The maximum compensation current value is used as the output limit of the PID controller to prevent current oscillation caused by over-regulation.

[0076] Through the above solutions, the present application realizes the stable operation of the magnetic levitation motor of the low-power distilled water machine under the scenarios of power grid voltage fluctuation and multiple noise thresholds. The collaborative mechanism of voltage fluctuation detection and scenario coding recognition enables the control parameters to quickly adapt to the power grid state and environmental requirements. The combination of current gradient limitation and dynamic adjustment of PID parameters effectively suppresses electromagnetic noise while ensuring the stability of the motor. This solution overcomes the limitations of traditional fixed-parameter control under complex working conditions and improves the adaptability and reliability of the low-power distilled water machine in noise-sensitive scenarios such as medical treatment and laboratories.

[0077] In some of the above solutions of the present application, a method for retrieving control parameters from a preset parameter matrix based on voltage fluctuation patterns and scenario coding is proposed to achieve dynamic control of the motor. However, due to the actual changes in parameters such as winding temperature, speed, and stator current during the operation of the motor, key parameters such as stator resistance, mutual inductance, and back electromotive force coefficient of the motor will drift. If the initial preset parameter matrix is still directly used, it will lead to the mismatch between the retrieved maximum compensation current value, current change rate threshold, and PID integral time constant and the actual working conditions, thereby causing problems such as cumulative control errors, decreased motor operation stability, and failure of noise suppression.

[0078] In response to this, the present application further proposes: real-time collecting motor operation data, where the motor operation data includes motor winding temperature, motor speed, and stator current; calculating the motor parameter drift amount based on the motor winding temperature, motor speed, and stator current, where the motor parameters include stator resistance, mutual inductance, and back electromotive force coefficient; updating the preset parameter matrix using the least squares method according to the motor parameter drift amount, where the preset parameter matrix includes the maximum compensation current value, current change rate threshold, and PID integral time constant under different combinations of voltage fluctuation patterns and scenario coding; and querying the updated preset parameter matrix in response to the changes in voltage fluctuation patterns and scenario coding to obtain the corresponding maximum compensation current value, current change rate threshold, and PID integral time constant.

[0079] Among them, the acquisition of the motor winding temperature can be achieved through NTC temperature sensors buried in the stator slots; the motor speed is measured by Hall sensors or optical encoders; the stator current is collected by current transformers, and its range needs to cover 120% of the rated current of the motor. The parameter drift amount is calculated using the Kalman filter algorithm. By establishing an observation equation that includes the temperature-resistance transfer function and the dynamic model of the speed-back electromotive force coefficient, the motor winding temperature is used as the main variable of the resistance drift amount, and the motor speed is used as the correction factor for the back electromotive force coefficient drift amount. The preset parameter matrix adopts a two-dimensional index structure. The row vector corresponds to the classification of voltage fluctuation modes, and the column vector corresponds to the hierarchical encoding of scenarios. The matrix elements store the normalized control parameter combinations. During the least squares update process, the parameter drift amount is converted into the residual term of the parameter matrix, and the objective function converges to the error tolerance range through iterative calculation.

[0080] Specifically, the motor winding temperature, speed, and stator current data are synchronously input into the parameter drift amount estimation module. Among them, after the temperature data is processed by moving average filtering, it is substituted into the stator resistance temperature coefficient formula to calculate the real-time resistance value, and the deviation from the initial resistance value is used as the resistance drift amount; the speed data is used to correct the speed-voltage relationship in the back electromotive force coefficient model, and the parameter coupling error caused by speed fluctuation is eliminated through the dynamic compensation algorithm. During the parameter matrix update process, the least squares method performs a linear regression analysis on each control parameter in the matrix with the current parameter drift amount as the constraint condition. For example, a negative correlation is established between the maximum compensation current value and the stator resistance drift amount, and a positive correlation is established between the current change rate threshold and the back electromotive force coefficient drift amount. The updated parameter matrix is optimized for storage through a hash table. The combination of voltage fluctuation mode and scenario encoding is converted into a hash key value to achieve fast retrieval of control parameters. When a change in the voltage fluctuation mode or scenario encoding is detected, the updated parameter matrix is reloaded into the control decision module to ensure that the PID integral time constant matches the current electromagnetic characteristics of the motor, thereby suppressing the current oscillation and electromagnetic noise caused by parameter mismatch.

[0081] Through the above technical solutions, this application realizes the dynamic compensation of the influence of the drift amounts of the stator resistance, mutual inductance, and back electromotive force coefficient on the control parameters during the operation of the motor. By online optimizing the preset parameter matrix, the maximum compensation current value, current change rate threshold, and PID integral time constant are made to match the actual working conditions of the motor in real time, effectively solving the problems of current loop oscillation and torque ripple caused by parameter mismatch, and ensuring that the magnetic levitation motor maintains stable levitation and reduces high-frequency electromagnetic noise under the conditions of power grid voltage fluctuation and multi-noise scenarios.

[0082] In some of the above solutions of the present application, it is proposed to update the preset parameter matrix by using the least squares method according to the motor parameter drift amount to dynamically adjust the control parameters. However, in this process, the difference in the influence of different load states of the distilled water machine on the motor parameter drift is not considered. Since the low-power distilled water machine has significant differences in the temperature rise gradient of the motor winding and the current dynamic response characteristics when operating at low and high loads, if the same weight is used to process the parameter drift under all working conditions, it will cause the update of the parameter matrix to deviate from the actual operating state, thereby reducing the motor control accuracy and the noise suppression effect.

[0083] In response to this, the present application further proposes to obtain the current load state of the distilled water machine, where the load state includes the water level and the water temperature; based on the water level and the water temperature, determine the motor parameter drift weight coefficient corresponding to the current load state, and the motor parameter drift weight coefficient includes the stator resistance weight coefficient, the mutual inductance weight coefficient, and the back electromotive force coefficient weight coefficient; according to the motor parameter drift amount and the motor parameter drift weight coefficient, use the weighted least squares method to update the preset parameter matrix, and the preset parameter matrix includes the maximum compensation current value, the current change rate threshold, and the PID integral time constant under different voltage fluctuation modes and scenario coding combinations.

[0084] Among them, the acquisition of the load state is realized by a water level sensor and a temperature sensor installed inside the water tank, and the water level sensor adopts the capacitive measurement principle. The determination of the motor parameter drift weight coefficient is realized by a preset two-dimensional mapping table, which takes the water level percentage and the water temperature in degrees Celsius as inputs and outputs the stator resistance weight coefficient, the mutual inductance weight coefficient, and the back electromotive force coefficient weight coefficient. As a preferred implementation, the water level is divided into three levels: low (<30%), medium (30%-70%), and high (>70%), and the water temperature is divided into three levels: low temperature (<60°C), medium temperature (60°C-80°C), and high temperature (>80°C). The corresponding value range of the stator resistance weight coefficient is 0.5-1.2, the mutual inductance weight coefficient is 0.8-1.5, and the back electromotive force coefficient weight coefficient is 1.0-2.0. The application of the weighted least squares method requires constructing a weighted sum of squared residuals function, and its weight matrix consists of three weight coefficients as diagonal matrix elements, and the specific form is W = diag(α_R, α_L, α_ψ), where α_R is the stator resistance weight coefficient, α_L is the mutual inductance weight coefficient, and α_ψ is the back electromotive force coefficient weight coefficient.

[0085] Specifically, when voltage fluctuations in the power grid are detected during the operation of the maglev motor, the current load status parameters of the distilled water machine are continuously collected through a water level sensor and a temperature sensor first. The measured water level value and water temperature value are input into a preset two-dimensional mapping table to query and obtain the corresponding three weight coefficients. Subsequently, the stator resistance drift ΔR, mutual inductance drift ΔL, and back electromotive force coefficient drift Δψ calculated in real time are respectively multiplied by the corresponding weight coefficients to form weighted drifts. During the parameter matrix update process, by constructing the objective function of the weighted least squares method, the control parameters in the updated parameter matrix can preferentially reflect the parameter drifts that have a significant impact on the current load status. For example, under the working conditions of high temperature and high water level, the weight coefficient of the back electromotive force coefficient is set to 2.0. At this time, the influence weight of Δψ in the parameter matrix update is doubled, so as to more accurately compensate for the demagnetization effect of the permanent magnet caused by high temperature. This process is combined with voltage fluctuation pattern recognition and scenario coding retrieval, so that the finally obtained maximum compensation current value, current change rate threshold, and PID integral time constant not only match the current power grid state but also adapt to the actual load conditions, thereby improving the motor control accuracy and effectively suppressing noise.

[0086] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0087] Obtain the current load status of the distilled water machine, including the water level and water temperature. The water level is detected by a water level sensor, and the water temperature is detected by a temperature sensor. For example, the water level is 80%, and the water temperature is 60°C.

[0088] Based on the water level and water temperature, determine the motor parameter drift weight coefficients corresponding to the current load status. Specifically, establish a mapping table between the load status and the motor parameter drift weight coefficients. When the water level is 80% and the water temperature is 60°C, look up the table to obtain the stator resistance weight coefficient of 0.8, the mutual inductance weight coefficient of 0.6, and the back electromotive force coefficient weight coefficient of 0.7.

[0089] According to the motor parameter drifts and the motor parameter drift weight coefficients, update the preset parameter matrix using the weighted least squares method. Among them, the motor parameter drifts are calculated from the motor operation data collected in real time. For example, the stator resistance drift is 5%, the mutual inductance drift is 3%, and the back electromotive force coefficient drift is 4%. Multiply the motor parameter drifts by the corresponding weight coefficients to obtain the weighted drifts. Then construct the least squares objective function, with the maximum compensation current value, current change rate threshold, and PID integral time constant in the preset parameter matrix as independent variables and the weighted drifts as constraint conditions, and solve the objective function to obtain the updated preset parameter matrix.

[0090] Through the above technical solutions, the present application can dynamically adjust the weight of the motor parameter drift according to the actual load state of the distilled water machine, improving the accuracy and pertinence of the parameter matrix update. Thus, under different load conditions, the control system can better adapt to the actual drift of the motor parameters, improve the motor control accuracy, and effectively suppress noise. By considering the influence of the load state on the motor parameter drift, this solution avoids the problem that the parameter matrix update deviates from the actual operating state caused by using a unified weight to process the parameter drift under all working conditions, thereby improving the adaptability and stability of the low-power distilled water machine in complex power grid environments and multi-noise threshold scenarios.

[0091] In some of the above solutions of the present application, when collecting grid voltage data in real time, due to the existence of high-frequency noise and instantaneous interference in the grid signal, directly calculating the voltage change rate will lead to misjudgment of the voltage fluctuation pattern. At the same time, traditional detection methods cannot distinguish between rapid voltage fluctuations and continuous low-frequency disturbances, resulting in inaccurate matching of control parameters.

[0092] In response to this, the present application further proposes that the steps of detecting the grid voltage change rate and determining the voltage fluctuation pattern based on a preset threshold include: collecting grid voltage data in real time, and filtering the grid voltage data using a moving average filtering algorithm; calculating the change rate of the filtered voltage data within a preset time window to obtain the voltage change rate; performing spectral analysis on the voltage change rate to extract the main frequency component of the voltage fluctuation; when the voltage change rate exceeds the preset threshold and the main frequency component is lower than the preset frequency threshold, determining the voltage fluctuation pattern.

[0093] Among them, the moving average filtering algorithm performs a moving average process on the grid voltage data by setting a moving window with a length of 5 - 15 sampling periods. The preset time window is set in the range of 20 - 50 ms, and this range can balance the capture of the voltage fluctuation trend and the requirement for data stability. The spectral analysis can use the wavelet transform algorithm to extract the amplitude of the main frequency component within the frequency band of 0.1 - 5 Hz, and this frequency band covers the characteristic frequencies of typical grid low-frequency disturbances. The judgment condition that the main frequency component is lower than the preset frequency threshold forms a joint constraint with the time-domain criterion for the voltage change rate exceeding the limit.

[0094] Specifically, after the grid voltage data is filtered by moving average filtering, the high-frequency noise components are filtered out, and the remaining voltage signal fluctuation trend is used to calculate the change rate through a preset time window. After obtaining the voltage change rate, the main frequency component of the fluctuation is separated through spectrum analysis. When the main frequency component is in the low-frequency range and the change rate exceeds the preset threshold, it is determined as the grid persistent low-frequency disturbance mode. For example, when the detected voltage change rate is 5V / s and the main frequency component is 3Hz, it is compared with the preset threshold of 3V / s and the frequency threshold of 10Hz. After meeting the dual conditions, the corresponding control strategy is triggered. This combined determination method effectively distinguishes the high-frequency transient characteristics of voltage sag events from the low-frequency persistent characteristics of low-frequency oscillations through time-frequency joint analysis, avoiding the misjudgment risk under a single threshold criterion.

[0095] Through the above technical solutions, the present application realizes the accurate identification of grid voltage fluctuations. Moving average filtering effectively eliminates high-frequency noise and instantaneous interference, and retains the true voltage fluctuation trend. The setting of the preset time window balances the response speed and data stability. Spectrum analysis analyzes the fluctuation characteristics from both the time domain and the frequency domain, distinguishing sudden voltage sags from grid low-frequency oscillations. The combined determination logic overcomes the defect of easy misjudgment in traditional single-threshold judgment, provides a reliable input for subsequent parameter matrix retrieval, and ensures the accuracy of dynamic adjustment of control parameters.

[0096] In some of the above solutions of the present application, a solution of updating the preset parameter matrix by combining the weighted least squares method with the load state weight coefficient is proposed. However, under the constraints of dynamic grid fluctuations and multi-scenario noise, it is difficult to effectively balance the coupling relationship between different control parameters only by linearly weighting the parameter drift amount with the weight coefficient, which easily leads to the objective function converging to a local optimal solution, and then resulting in insufficient accuracy of the updated preset parameter matrix, affecting the global minimization of the motor control error.

[0097] In response to this, the present application further proposes: constructing an objective function, where the objective function takes the maximum compensation current value, the current change rate threshold, and the PID integral time constant in the preset parameter matrix as independent variables, and the motor parameter drift amount and the motor parameter drift weight coefficient as constraint conditions, and the objective function is used to minimize the motor control error; using the sequential quadratic programming algorithm to solve the objective function to obtain the updated preset parameter matrix, and the preset parameter matrix includes the maximum compensation current value, the current change rate threshold, and the PID integral time constant under different voltage fluctuation modes and scenario coding combinations.

[0098] Among them, when constructing the objective function, the maximum compensation current value, the current change rate threshold, and the PID integral time constant are used as multi-dimensional optimization variables. The physical change range of the constraint parameters is restricted by the motor parameter drift amount. For example, the stator resistance drift amount is limited within ±5% of the nominal value, and the back electromotive force coefficient drift amount is restricted by ±3% of the nominal value. At the same time, the motor parameter drift weight coefficient is introduced as a Lagrange multiplier into the constraint condition. In specific implementation, the value range of the stator resistance weight coefficient is 0.6 - 1.2, and the back electromotive force coefficient weight coefficient is 0.8 - 1.5, and its value is dynamically adjusted according to the load state. The sequential quadratic programming algorithm approximates the optimal solution by iteratively solving the quadratic sub-problem. Each iteration requires calculating the Hessian matrix and gradient vector of the objective function, and performing a feasible direction search in combination with the Jacobian matrix of the constraint condition, and finally converges to the global optimal solution within 3 - 5 iterations.

[0099] Specifically, under the constraints of grid voltage fluctuation and multi-scenario noise, first, the control parameters in the preset parameter matrix are defined as optimization variables, and the inequality constraint boundary is established through the motor parameter drift amount. For example, the maximum compensation current value shall not exceed 120% of the rated current. Subsequently, the weight coefficient corresponding to the load state is used as a scaling factor for the constraint condition. For example, when the load water temperature is higher than 60°C, the back electromotive force coefficient weight coefficient is increased to 1.2 to strengthen the suppression of temperature drift. The objective function uses the root mean square value of the motor control error as the optimization index, and its mathematical expression is the integral sum of squares of the current tracking error under each working condition. When solving through the sequential quadratic programming algorithm, the non-linear constraint needs to be transformed into a quadratic programming sub-problem, and the active set method is used to process the active constraints, and finally, an updated parameter matrix that meets the global optimum is output. Thus, the update process of the parameter matrix not only ensures the physical realizability of the control parameters but also overcomes the local optimum trap through non-linear optimization, enabling the motor control error to converge to the global minimum under multiple working conditions.

[0100] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0101] Construct the objective function J(x), where x is a vector composed of the maximum compensation current value, the current change rate threshold, and the PID integral time constant in the preset parameter matrix. The expression of the objective function is:

[0102] J(x) = w1*(Imax - Iref)^2 + w2*(dI / dt - dIref / dt)^2 + w3*(Ti - Tiref)^2

[0103] Among them, Imax, dI / dt, and Ti are the maximum compensation current value, the current change rate threshold, and the PID integral time constant respectively, Iref, dIref / dt, and Tiref are the corresponding reference values, and w1, w2, and w3 are weight coefficients.

[0104] The constraint conditions include:

[0105] g1(x): |Rs - Rsref| <= ΔRs_max;

[0106] g2(x): |L - Lref| <= ΔL_max;

[0107] g3(x): |Ke - Keref| <= ΔKe_max;

[0108] where Rs, L, and Ke are the stator resistance, mutual inductance, and back electromotive force coefficient respectively, Rsref, Lref, and Keref are the corresponding reference values, and ΔRs_max, ΔL_max, and ΔKe_max are the allowed maximum deviations.

[0109] The sequential quadratic programming algorithm is used to solve this optimization problem. Through iterative solution, an updated preset parameter matrix is finally obtained, which includes the maximum compensation current value, current change rate threshold, and PID integral time constant under different voltage fluctuation modes and scenario coding combinations.

[0110] Through the above technical solution, the present application realizes the accurate update of the preset parameter matrix under dynamic grid fluctuations and multi-scenario noise constraints. Since the sequential quadratic programming algorithm is used to solve the non-linear constraint optimization problem, the problem of local optimal solutions caused by only linear weighting through weight coefficients is avoided. At the same time, by constructing an objective function including the maximum compensation current value, current change rate threshold, and PID integral time constant, and introducing the motor parameter drift amount and drift weight coefficient as constraint conditions, the coupling relationship between different control parameters is effectively balanced. This method improves the accuracy of the update of the preset parameter matrix, and thus realizes the global minimization of the motor control error, enhancing the operation stability and silent performance of the low-power distilled water machine in a complex grid environment and multi-noise scenarios.

[0111] In some of the above solutions of the present application, a scheme for retrieving the preset parameter matrix through the combination of voltage fluctuation mode and scenario coding is proposed to dynamically adjust the control parameters. However, in a complex grid environment, when there are multi-dimensional parameter intersections in the combination of voltage fluctuation mode and scenario coding, the traditional matrix retrieval method may cause delays in obtaining control parameters due to low multi-dimensional data matching efficiency, thus affecting the real-time performance of motor control.

[0112] In response to this, the present application further proposes: determining the voltage level corresponding to the voltage fluctuation pattern and the noise level corresponding to the scenario code; based on the voltage level and the noise level, performing a match in the index table of the preset parameter matrix, where the index table stores the mapping relationship between the voltage level, the noise level, and the parameter storage address; in response to the existence of a parameter storage address in the index table that matches both the voltage level and the noise level, reading the maximum compensation current value, the current change rate threshold, and the PID integral time constant from the parameter storage address.

[0113] Among them, the division of the voltage level is based on the discretization interval of the voltage fluctuation amplitude. For example, the power grid voltage fluctuation range is divided into three levels: within ±5% is the first level, ±5% - 10% is the second level, and exceeding ±10% is the third level. The determination of the noise level is based on the environmental noise threshold parsed from the scenario code. For example, the medical scenario corresponds to the first level of low noise, the laboratory scenario corresponds to the second level of medium noise, and the industrial scenario corresponds to the third level of high noise. The index table adopts a two-dimensional hash structure, with the combination of the voltage level and the noise level as the key value, directly mapping to the parameter storage address. For example, when the voltage level is the second level and the noise level is the first level, the hash key value can be encoded as "2 - 1", pointing to the corresponding physical storage address. The parameter storage address contains data blocks of the maximum compensation current value, the current change rate threshold, and the PID integral time constant. The size of each data block is fixed at 12 bytes, where the first 4 bytes store the maximum compensation current value, the middle 4 bytes store the current change rate threshold, and the last 4 bytes store the PID integral time constant. Through the combination of discretization grading and hash mapping, the multi-dimensional parameter matching is transformed into a two-dimensional key value query, avoiding traversing the entire parameter matrix, and the query time is shortened to the millisecond level.

[0114] Specifically, when the power grid voltage fluctuation exceeds the threshold, the voltage fluctuation pattern is classified into the corresponding voltage level. For example, when it is detected that the voltage dip amplitude is 8%, it is determined as the second voltage level. At the same time, the scenario code parsing unit reads the current environmental RFID tag information. For example, the medical area tag corresponds to the first level of noise. Based on the second voltage level and the first noise level, a hash key value "2 - 1" is generated in the index table, directly locating the physical address in the preset parameter matrix that stores the combined parameters. If there is a matching item in the index table, the maximum compensation current value of 25A, the current change rate threshold of 5A / ms, and the PID integral time constant of 0.2s are read from this address. Through the fast positioning of the hash index, the parameter retrieval time is reduced from 20ms of the traditional method to 2ms, meeting the real-time requirement of motor control. The read parameters act on the motor winding through the current gradient limiter and the PID controller. Among them, the maximum compensation current value limits the current amplitude to reduce electromagnetic noise, the current change rate threshold suppresses current mutation, and the PID integral time constant adjusts the control response speed, thereby realizing the stable operation of the motor under voltage fluctuation and multi-noise constraints.

[0115] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0116] In the magnetic levitation motor control system of a low-power distilled water machine, a voltage level classifier and a noise level classifier are set. The voltage level classifier divides the voltage fluctuation pattern into 5 levels, corresponding to voltage fluctuation amplitudes of 0 - 5%, 5 - 10%, 10 - 15%, 15 - 20%, and above 20% respectively. The noise level classifier divides the scenario code into 4 levels, corresponding to noise limit requirements of 30dB, 40dB, 50dB, and 60dB respectively.

[0117] A 20×20 parameter index table is established in advance. The rows represent voltage levels, and the columns represent noise levels. Each table cell stores the parameter storage address under the corresponding conditions, pointing to a parameter set containing the maximum compensation current value, the current change rate threshold, and the PID integral time constant.

[0118] During operation, first, the voltage level classifier is used to determine the voltage level corresponding to the current voltage fluctuation pattern. For example, if the detected voltage fluctuation amplitude is 12%, it is determined as the 3rd voltage level. At the same time, the noise level classifier is used to determine the noise level corresponding to the current scenario code. For example, if the detected scenario is a medical environment with a 40dB noise limit, it is determined as the 2nd noise level.

[0119] Next, locate the cell in the 3rd row and 2nd column of the parameter index table, and obtain the parameter storage address stored in this cell. This address points to a parameter set containing a maximum compensation current value of 2.5A, a current change rate threshold of 0.5A / ms, and a PID integral time constant of 15ms.

[0120] Finally, the control system directly reads these three parameter values from this address for subsequent motor control.

[0121] Through the above technical solution, the present application realizes fast parameter matching under voltage fluctuations and noise limits. Compared with the traditional multi-dimensional matrix retrieval method, this solution simplifies the complex multi-dimensional matching to one index table lookup and one parameter reading, greatly improving the parameter acquisition speed. This efficient parameter matching mechanism ensures that in the case of rapid voltage fluctuations in the power grid, the control system can timely adjust the control parameters, effectively suppressing the impact of voltage fluctuations on motor operation. At the same time, by precisely matching the control parameters corresponding to the noise level, precise noise control in different scenarios is achieved, meeting diverse application requirements. In addition, the index table structure of this solution has good scalability, and new voltage levels and noise levels can be easily added to adapt to more complex application environments.

[0122] In some of the above solutions of the present application, in view of the problem that in the scenarios of power grid voltage fluctuation and multiple noise thresholds of a low-power distilled water machine, the change rate of the motor winding current exceeds the threshold, resulting in excessive electromagnetic noise, and the voltage fluctuation affects the stable operation of the motor, a technical solution of dynamically adjusting the current gradient limit strategy is proposed.

[0123] For this, the present application further proposes the following technical means: calculating the difference between the actual change rate of the motor winding current and the current change rate threshold; when the difference is greater than zero, starting the current gradient limiter, and determining the PWM duty cycle adjustment rate in response to the voltage fluctuation mode, where the voltage dip mode corresponds to a fast adjustment rate and the voltage slow change mode corresponds to a gentle adjustment rate; determining the PWM switching frequency based on the scenario encoding; adjusting the PWM duty cycle according to the PWM duty cycle adjustment rate, and driving the motor winding voltage with the PWM switching frequency to suppress the actual change rate of the motor winding current and make the actual change rate converge to the current change rate threshold.

[0124] Among them, the starting condition of the current gradient limiter is realized through real-time difference calculation, and the difference calculation module can be integrated into the signal processing unit of the motor controller. The determination of the PWM duty cycle adjustment rate depends on the classification result of the voltage fluctuation mode. In the voltage dip mode, an adjustment rate with a millisecond-level response speed is adopted. For example, the duty cycle change rate is set to 2000 times per second, while in the voltage slow fluctuation mode, it is adjusted to 500 times per second to match different dynamic requirements. The selection of the PWM switching frequency is based on the noise level corresponding to the scenario encoding. For example, the medical scenario corresponds to a high-frequency switch (above 20 kHz), and the laboratory scenario uses a medium frequency (15 kHz) to reduce audible noise through frequency switching. The application of the PID integral time constant further combines the dynamic adjustment of the compensation current. For example, when the voltage drops suddenly, the integral time is shortened to 10 ms to accelerate the response, while when it fluctuates slowly, it is extended to 50 ms to smooth the output.

[0125] Specifically, after detecting that the actual current change rate exceeds the threshold, the current gradient limiter is activated. Based on the voltage fluctuation pattern recognition result, the corresponding duty cycle adjustment rate is selected: when there is a voltage dip, the duty cycle is adjusted quickly (for example, 2000 changes per second), and the winding voltage is rapidly reduced to suppress current mutation; when the voltage fluctuates slowly, a gentle adjustment rate is adopted (for example, 500 changes per second) to avoid excessive adjustment from causing high-frequency noise. At the same time, the scene coding analysis unit obtains the noise limit requirement of the current scene. For example, in a medical scene, the PWM switching frequency needs to be increased to above 20 kHz so that the electromagnetic noise frequency band is beyond the sensitive range of the human ear. The adjusted duty cycle signal and the switching frequency act on the motor drive circuit together to limit the current change rate within the threshold. Further, the PID controller corrects the output voltage in real time based on the maximum compensation current value and the integral time constant. For example, by shortening the integral time constant, the compensation response is accelerated to ensure current stability under voltage fluctuations. Through the above steps, both the response lag problem during voltage dips is avoided, and the electromagnetic noise in the gentle fluctuation scenario is reduced, achieving a balance between dynamic response and noise suppression.

[0126] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0127] Calculate the difference between the actual change rate of the motor winding current and the current change rate threshold. The motor controller uses a high-precision current sampling circuit with a sampling frequency set to 20 kHz, and the actual current change rate is obtained through the differential calculation of two consecutive sampled current values. The current change rate threshold is obtained by looking up a table. For example, in a medical scene, it is set to 0.5 A / ms.

[0128] When the difference is greater than zero, start the current gradient limiter. The current gradient limiter is implemented using FPGA. In response to the voltage fluctuation pattern, determine the PWM duty cycle adjustment rate. The voltage dip mode corresponds to a fast adjustment rate, set to 10% / ms; the slow voltage change mode corresponds to a gentle adjustment rate, set to 1% / ms.

[0129] Based on the scene coding, determine the PWM switching frequency. For example, the medical scene corresponds to 20 kHz, the laboratory scene corresponds to 15 kHz, and the office area scene corresponds to 10 kHz.

[0130] Adjust the PWM duty cycle according to the PWM duty cycle adjustment rate. The piecewise linear interpolation algorithm is used to divide the duty cycle adjustment into 10 levels, and each level corresponds to an adjustment time of 0.1 ms. Drive the motor winding voltage with the PWM switching frequency to suppress the actual change rate of the motor winding current and make the actual change rate converge to the current change rate threshold.

[0131] Through the above technical solutions, the present application realizes the adaptive adjustment of the motor control strategy to the voltage fluctuation mode. In the case of a voltage dip, the PWM duty cycle is quickly adjusted to effectively suppress the risk of motor instability; when the voltage changes slowly, a gentle adjustment strategy is adopted to avoid introducing additional electromagnetic noise. At the same time, the PWM switching frequency is dynamically adjusted through scene coding to achieve precise control of motor noise in different noise sensitivity scenarios. Thus, while ensuring the stable operation of the motor, this solution meets the differentiated requirements for noise control in different application scenarios and improves the adaptability of the low-power distilled water machine in complex power grid environments and multi-scenario applications.

[0132] In some of the above solutions of the present application, a method of reducing the motor winding voltage by adjusting the PWM duty cycle to suppress the current change rate is proposed. However, in the specific implementation process, due to different voltage fluctuation modes (such as voltage dips and slow changes) having different requirements for the duty cycle adjustment rate, using a single adjustment rate will result in a response lag in the voltage dip scenario or over-adjustment in the slow change scenario; at the same time, the noise levels corresponding to different scene encodings have different sensitivities to the PWM switching frequency. If the switching frequency is not dynamically adjusted according to the scene, problems such as excessive high-frequency switching noise or insufficient current suppression effect caused by low-frequency switching may occur.

[0133] In response to this, the present application further proposes that when the difference is greater than zero, start the current gradient limiter, determine the PWM duty cycle adjustment rate in response to the voltage fluctuation mode, with a fast adjustment rate corresponding to the voltage dip mode and a gentle adjustment rate corresponding to the slow voltage change mode; determine the PWM switching frequency based on the scene coding; adjust the PWM duty cycle according to the PWM duty cycle adjustment rate, and drive the motor winding voltage with the PWM switching frequency to suppress the actual change rate of the motor winding current and make the actual change rate converge to the current change rate threshold.

[0134] Among them, the determination of the PWM duty cycle adjustment rate can be achieved by setting multi-level rate thresholds. For example, in the voltage sag mode, the duty cycle adjustment rate is set to change by 5%-10% per millisecond, and in the voltage slow change mode, it is adjusted to 0.5%-1% per millisecond. The determination of the PWM switching frequency is based on the noise level and frequency mapping table preset in the scenario encoding. For example, the medical scenario corresponds to a high frequency above 20 kHz to avoid the sensitive frequency band of the human ear, and the industrial scenario allows a low frequency below 15 kHz to reduce switching losses. When the current gradient limiter is started, the microcontroller compares the actual current change rate with the threshold difference in real time. If the difference exceeds zero, the mode judgment logic is triggered, and the adjustment rate is switched in combination with the mode signal output by the voltage fluctuation detection module. The linkage control of the PWM duty cycle adjustment rate and the switching frequency is achieved through a hardware timer. For example, in the fast adjustment mode, a timer resolution of 1 μs level is adopted, and in the smooth mode, it is switched to a 10 μs level resolution to reduce the calculation load. The synergistic effect of the duty cycle adjustment rate and the scenario encoding is reflected in that when the scenario encoding indicates a high-noise sensitive environment, even in the voltage sag mode, the switching frequency still gives priority to meeting the noise limit requirements, and at the same time, the current suppression effect is compensated by increasing the duty cycle adjustment rate.

[0135] Specifically, after the current gradient limiter is started, the voltage fluctuation mode is monitored in real time. If the detected voltage change rate exceeds 500 V / s, it is determined as the sag mode. At this time, the duty cycle adjustment rate is set to a linear change rate of 8% per millisecond, and the PWM switching frequency is set to 22 kHz according to the medical area label information in the scenario encoding. When the voltage change rate is lower than 50 V / s, it is switched to the smooth mode, the duty cycle adjustment rate is reduced to 0.8% per millisecond, and the switching frequency is adjusted to 12 kHz according to the industrial scenario encoding. The PWM duty cycle is achieved by adjusting the conduction time ratio of the pulse width modulation signal. For example, in the sag mode, it only takes 1.25 ms to quickly reduce the duty cycle from 75% to 65%, while the same amplitude adjustment takes 10 ms to complete in the smooth mode. The adjusted PWM signal is applied to the motor winding through the drive circuit, so that the reduction amplitude of the winding voltage matches the adjustment rate, thereby controlling the current change rate within the threshold range. This dynamic adjustment mechanism shortens the current suppression response time to 1 / 5 of the traditional method when the voltage sags by eliminating the hysteresis effect of the fixed parameter control, and reduces the electromagnetic noise by more than 12 dB in the noise-sensitive scenario.

[0136] As a preferred embodiment, the solution of the present application is specifically implemented as follows:

[0137] In the magnetic levitation motor control system of a low-power distilled water machine, a voltage fluctuation detection module and a scenario coding analysis module are set up. The voltage fluctuation detection module uses a high-speed sampling circuit with a sampling frequency of 10 kHz to detect the rate of change of the grid voltage. When the rate of voltage change exceeds 100 V / s, it is determined as the voltage dip mode; when the rate of voltage change is between 10 - 100 V / s, it is determined as the slow voltage change mode. The scenario coding analysis module obtains the current scenario coding by reading the environmental RFID tag information. For example, the medical scenario coding is 01, the laboratory scenario coding is 02, and the office area scenario coding is 03.

[0138] The control system determines the PWM duty cycle adjustment rate according to the voltage fluctuation mode. The PWM duty cycle adjustment rate corresponding to the voltage dip mode is 50% / ms, and the PWM duty cycle adjustment rate corresponding to the slow voltage change mode is 10% / ms. At the same time, the PWM switching frequency is determined based on the scenario coding, corresponding to 20 kHz for the medical scenario, 15 kHz for the laboratory scenario, and 10 kHz for the office area scenario.

[0139] The control system uses a digital signal processor (DSP) to implement PWM duty cycle adjustment and switching frequency control. The DSP outputs the PWM waveform through a 12-bit DAC with a resolution of 0.024%. The PWM waveform is amplified by the drive circuit and then drives the motor winding. The DSP monitors the motor winding current in real time with a sampling frequency of 100 kHz. When the detected actual current change rate exceeds the preset threshold, the DSP adjusts the PWM duty cycle according to the determined PWM duty cycle adjustment rate and drives the motor winding voltage at the determined PWM switching frequency.

[0140] For example, when a voltage dip occurs in the medical scenario, the DSP reduces the PWM duty cycle at a rate of 50% / ms and sets the PWM switching frequency to 20 kHz at the same time. The sudden change of current is suppressed by quickly adjusting the PWM duty cycle, and the high-frequency switching effectively reduces the electromagnetic noise. The DSP continuously monitors the current change rate until it converges to the preset threshold.

[0141] Through the above technical solutions, this application realizes the adaptive adjustment of motor control parameters to grid voltage fluctuations and application scenarios. Through the differentiated PWM duty cycle adjustment rate, it quickly responds to voltage dips and avoids the noise caused by sudden current changes; for slow voltage changes, a gentle adjustment rate is adopted to maintain the stability of motor operation. At the same time, the PWM switching frequency is dynamically adjusted according to the scenario requirements, effectively controlling the electromagnetic noise in different scenarios while ensuring the current suppression effect. This adaptive control mechanism breaks through the limitations of traditional fixed-parameter control and realizes the stable operation and dynamic noise control of the magnetic levitation motor of the low-power distilled water machine under grid voltage fluctuations and multi-noise threshold scenarios.

[0142] Refer toFigure 2 , this application further proposes a magnetic levitation motor control device for controlling the operation of the magnetic levitation motor of a low-power distilled water machine in scenarios where the grid voltage fluctuation exceeds a threshold and the multi-noise threshold, including:

[0143] A voltage detection unit 210 for detecting the rate of change of the grid voltage and determining the voltage fluctuation mode based on a preset threshold;

[0144] A scenario coding analysis unit 220 for reading the environmental RFID tag information to obtain the current scenario coding;

[0145] A parameter retrieval unit 230 for retrieving the maximum compensation current value, the current change rate threshold, and the PID integral time constant from a preset parameter matrix based on the voltage fluctuation mode and the scenario coding;

[0146] A control execution unit 240 for limiting the rate of change of the motor winding current according to the current change rate threshold and performing motor control through the maximum compensation current value and the PID integral time constant.

[0147] By detecting the grid voltage fluctuation mode and dynamically adjusting the control parameters in combination with the scenario coding, current mutation is suppressed and electromagnetic noise is reduced. At the same time, the parameter matrix is optimized according to the load state and environmental factors to improve the operation stability of the motor under different working conditions.

[0148] In addition, in some preferred embodiments, a magnetic levitation motor control device proposed by this application can execute the steps of any one of the above methods.

[0149] This application further proposes a magnetic levitation motor to which the above control method is applied.

[0150] This application realizes the deep integration of the magnetic levitation motor control strategy and the characteristics of the motor body. This solution can dynamically adjust the control parameters according to the grid voltage fluctuation and the operation scenario, effectively solving the contradiction between multi-scenario silent control and stability under voltage fluctuation. By combining the control method with the physical structure of the magnetic levitation motor, a closed-loop matching mechanism between the control strategy and the characteristics of the motor body is constructed, ensuring that the parameter update does not exceed the linear working area of the magnetic levitation bearing, thereby realizing the coordinated optimization of control stability and multi-scenario silent constraints. This mechatronic design breaks through the limitation of the decoupling of the traditional control strategy and the motor body parameters, improving the adaptability and performance of the magnetic levitation motor in complex grid environments and multi-noise threshold scenarios.

[0151] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A magnetic levitation motor control method, used for the operation control of the magnetic levitation motor of a low-power distilled water machine in a scenario where the grid voltage fluctuation exceeds a threshold and multiple noise thresholds, characterized in that: The following steps are involved: Detecting the rate of change of grid voltage and determining the voltage fluctuation mode based on a preset threshold; The scene code parsing unit reads the environmental RFID tag information to obtain the current scene code; Based on the voltage fluctuation pattern and scenario coding, the maximum compensation current value, current change rate threshold and PID integration time constant are retrieved from the preset parameter matrix; Limit the rate of change of the motor winding current according to the current change rate threshold, and perform motor control through the maximum compensation current value and the PID integral time constant; The step of retrieving the maximum compensation current value, the current change rate threshold and the PID integral time constant from the preset parameter matrix based on the voltage fluctuation mode and the scene coding includes: Determine the voltage level corresponding to the voltage fluctuation pattern and the noise level corresponding to the scene coding; Based on the voltage level and the noise level, matching is performed in an index table of a preset parameter matrix, wherein the index table stores a mapping relationship between the voltage level, the noise level and the parameter storage address; In response to the presence of a parameter storage address in the index table that matches both the voltage level and the noise level, a maximum compensation current value, a current change rate threshold, and a PID integral time constant are read from the parameter storage address.

2. A magnetic levitation motor control method according to claim 1, characterized in that: The step of retrieving the maximum compensation current value, the current change rate threshold and the PID integral time constant from the preset parameter matrix based on the voltage fluctuation mode and the scene coding includes: Collecting motor operation data in real time, the motor operation data including motor winding temperature, motor speed and stator current; Calculating the motor parameter drift based on the motor winding temperature, motor speed and stator current, wherein the motor parameters include stator resistance, mutual inductance and back electromotive force coefficient; According to the motor parameter drift, the least square method is used to update the preset parameter matrix, wherein the preset parameter matrix includes the maximum compensation current value, the current change rate threshold value and the PID integral time constant under different voltage fluctuation modes and scene coding combinations; In response to changes in the voltage fluctuation mode and the scene coding, the updated preset parameter matrix is ​​queried to obtain the corresponding maximum compensation current value, current change rate threshold and PID integral time constant.

3. A magnetic levitation motor control method according to claim 2, characterized in that: The step of updating a preset parameter matrix using the least square method according to the motor parameter drift, wherein the preset parameter matrix includes the maximum compensation current value, the current change rate threshold and the PID integral time constant under different voltage fluctuation modes and scene coding combinations includes: Obtaining the current load status of the distilled water machine, wherein the load status includes water level and water temperature; Based on the water level and the water temperature, determining a motor parameter drift weight coefficient corresponding to the current load state, wherein the motor parameter drift weight coefficient includes a stator resistance weight coefficient, a mutual inductance weight coefficient, and a back electromotive force weight coefficient; According to the motor parameter drift amount and the motor parameter drift weight coefficient, the weighted least squares method is used to update the preset parameter matrix, which includes the maximum compensation current value, the current change rate threshold and the PID integral time constant under different voltage fluctuation modes and scene coding combinations.

4. A magnetic levitation motor control method according to claim 1, characterized in that: The step of detecting the grid voltage change rate and determining the voltage fluctuation mode based on a preset threshold comprises: Collect grid voltage data in real time and filter it using a sliding average filtering algorithm; Calculate the change rate of the filtered voltage data within a preset time window to obtain the voltage change rate; Perform spectrum analysis on the voltage change rate and extract the main frequency component of voltage fluctuation; When the voltage change rate exceeds a preset threshold and the main frequency component is lower than a preset frequency threshold, the voltage fluctuation mode is determined.

5. A magnetic levitation motor control method according to claim 3, characterized in that: The step of updating a preset parameter matrix using a weighted least square method according to the motor parameter drift amount and the motor parameter drift weight coefficient, wherein the preset parameter matrix includes a maximum compensation current value, a current change rate threshold value and a PID integral time constant under different voltage fluctuation modes and scene coding combinations includes: Constructing an objective function, wherein the objective function takes the maximum compensation current value, the current change rate threshold value and the PID integral time constant in the preset parameter matrix as independent variables, takes the motor parameter drift amount and the motor parameter drift weight coefficient as constraint conditions, and the objective function is used to minimize the motor control error; A sequential quadratic programming algorithm is used to solve the objective function and obtain an updated preset parameter matrix, which includes a maximum compensation current value, a current change rate threshold and a PID integral time constant under different voltage fluctuation modes and scene coding combinations.

6. A magnetic levitation motor control method according to claim 1, characterized in that: The steps of limiting the motor winding current change rate according to the current change rate threshold and performing motor control through the maximum compensation current value and the PID integral time constant include: Calculate the difference between the actual rate of change of the motor winding current and the current rate of change threshold; When the difference is greater than zero, the current gradient limiter is started to reduce the motor winding voltage by adjusting the PWM duty cycle, thereby suppressing the actual change rate of the motor winding current and making the actual change rate converge to the current change rate threshold; Based on the maximum compensation current value, the target current value of the PID controller is calculated. According to the target current value and the PID integral time constant, the output voltage of the PID controller is adjusted to compensate for the influence of voltage fluctuation on the motor operation.

7. A magnetic levitation motor control method according to claim 6, characterized in that: The step of starting the current gradient limiter when the difference is greater than zero, reducing the motor winding voltage by adjusting the PWM duty cycle, suppressing the actual change rate of the motor winding current, and making the actual change rate converge to the current change rate threshold comprises: When the difference is greater than zero, the current gradient limiter is started, and the PWM duty cycle adjustment rate is determined in response to the voltage fluctuation mode, the voltage sag mode corresponds to a fast adjustment rate, and the voltage slow change mode corresponds to a gentle adjustment rate; Based on the scene code, determine the PWM switching frequency; The PWM duty cycle is adjusted according to the PWM duty cycle adjustment rate, and the motor winding voltage is driven at the PWM switching frequency to suppress the actual change rate of the motor winding current so that the actual change rate converges to the current change rate threshold.

8. A magnetic levitation motor control device, used for the operation control of the magnetic levitation motor of a low-power distilled water machine in a scenario where the grid voltage fluctuation exceeds a threshold and multiple noise thresholds, characterized in that: include: A voltage detection unit, used to detect the rate of change of the grid voltage and determine the voltage fluctuation mode based on a preset threshold; A scene code parsing unit is used to read the environmental RFID tag information and obtain the current scene code; A parameter retrieval unit, used to retrieve a maximum compensation current value, a current change rate threshold, and a PID integral time constant from a preset parameter matrix based on a voltage fluctuation pattern and a scenario code; A control execution unit, used for limiting the rate of change of the motor winding current according to a current change rate threshold value, and executing motor control through a maximum compensation current value and a PID integral time constant; The step of retrieving the maximum compensation current value, the current change rate threshold and the PID integral time constant from the preset parameter matrix based on the voltage fluctuation mode and the scene coding includes: Determine the voltage level corresponding to the voltage fluctuation pattern and the noise level corresponding to the scene coding; Based on the voltage level and the noise level, matching is performed in an index table of a preset parameter matrix, wherein the index table stores a mapping relationship between the voltage level, the noise level and the parameter storage address; In response to the presence of a parameter storage address in the index table that matches both the voltage level and the noise level, a maximum compensation current value, a current change rate threshold, and a PID integral time constant are read from the parameter storage address.

9. A magnetic levitation motor, characterized in that: The magnetic levitation motor is applied with the method according to any one of claims 1 to 7.

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